(1) Field of the Invention
The present invention relates to object detection in audio and visual imagery and, more specifically, to an actionable intelligence method and system which recognizes entities in an imagery signal, detects and classifies anomalous entities, and learns new hierarchal relationships between different classes of entities.
(2) Description of Related Art
Actionable intelligence is not new in the art. The term actionable intelligence denotes processing audio or video data and identifying entities that require action. Several actionable intelligence systems currently exist. One such system fuses video, radar, and other modalities to track and recognize anomalous vessel activities, however, this system lacks the ability to extract rules from the recognized vessel tracks, and is further limited to recognition of individual objects which renders it unsuitable for modeling group behaviors such as congregation/dispersion and leading/following. The system further fails to address different classes of abnormal behavior, such as fast versus slow approach. These limitations are addressed piecemeal by other systems. Other actionable intelligence systems are limited by requiring active search by system users, or focusing exclusively on specific tasks such as epidemiology (study of spreading diseases). Still other actionable intelligence systems are aimed at business or marketing decisions, where text or database information is considerably more structured and perhaps less dynamic than imagery and video data. There are also a number of other algorithms/modules which do not consider system integration at all, focusing exclusively on video surveillance, novelty detection, or rule extraction.
Thus, a continuing need exists for an actionable intelligence system which integrates the ability to (1) understand objects, patterns, events and behaviors in vision data; (2) translate this understanding into timely recognition of novel and anomalous entities and; (3) discover underlying hierarchical relationships between disparate labels entered by multiple users to provide a consistent and constantly evolving data representation.
(3) Other References Cited
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